WOS: 000272874100003In this study, identification of nonlinear systems via Laguerre network based fuzzy model is introduced. We first describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. The proposed approach is applied in three dynamic system modeling problems including Box-Jenkins gas furnace data and forced Van der Pol oscillator. When we compare the performance of the proposed approach against the classical Sugeno and adaptive network based fuzzy inference system modeling, our approach is found to have superior modeling performance and generalization capability. (C) 2009 Elsevier B.V. All rights reserved
This paper presents a methodology for generating data for training a fuzzy relational model, one neu...
A nonlinear black-box structure for a dynamical system is a model structure that is prepared to desc...
<p>Most real-world processes have nonlinear and complex dynamics. Conventional methods of cons...
WOS: 000272874100003In this study, identification of nonlinear systems via Laguerre network based fu...
WOS: 000287157400005The aim of the online nonlinear system identification is the accurate modeling o...
Identification and control of general nonlinear systems is a difficult but important problem. Variou...
AbstractMultilayer neural networks with error back-propagation learning algorithms have the capabili...
This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear sys...
Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification...
A Fuzzy logic system has been shown to be able to arbitrarily approximate any nonlinear function and...
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing ...
Abstract. In this paper, major properties of an adaptive fuzzy model as a system identifier when tra...
In this paper, two mathematical ways of building a fuzzy model of both linear and nonlinear systems ...
This paper provides an overview of system identification using orthonormal basis function models, su...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
This paper presents a methodology for generating data for training a fuzzy relational model, one neu...
A nonlinear black-box structure for a dynamical system is a model structure that is prepared to desc...
<p>Most real-world processes have nonlinear and complex dynamics. Conventional methods of cons...
WOS: 000272874100003In this study, identification of nonlinear systems via Laguerre network based fu...
WOS: 000287157400005The aim of the online nonlinear system identification is the accurate modeling o...
Identification and control of general nonlinear systems is a difficult but important problem. Variou...
AbstractMultilayer neural networks with error back-propagation learning algorithms have the capabili...
This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear sys...
Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification...
A Fuzzy logic system has been shown to be able to arbitrarily approximate any nonlinear function and...
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing ...
Abstract. In this paper, major properties of an adaptive fuzzy model as a system identifier when tra...
In this paper, two mathematical ways of building a fuzzy model of both linear and nonlinear systems ...
This paper provides an overview of system identification using orthonormal basis function models, su...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
This paper presents a methodology for generating data for training a fuzzy relational model, one neu...
A nonlinear black-box structure for a dynamical system is a model structure that is prepared to desc...
<p>Most real-world processes have nonlinear and complex dynamics. Conventional methods of cons...